A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station
The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our ma...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1600 |
| _version_ | 1797536984396201984 |
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| author | Yu Kang Jie Chen Yang Cao Zhenyi Xu |
| author_facet | Yu Kang Jie Chen Yang Cao Zhenyi Xu |
| author_sort | Yu Kang |
| collection | DOAJ |
| description | The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf. |
| first_indexed | 2024-03-10T12:08:37Z |
| format | Article |
| id | doaj.art-6dd8a698d989467094327cd4d3b09a14 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:08:37Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6dd8a698d989467094327cd4d3b09a142023-11-21T16:23:57ZengMDPI AGRemote Sensing2072-42922021-04-01138160010.3390/rs13081600A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring StationYu Kang0Jie Chen1Yang Cao2Zhenyi Xu3Department of Automation, University of Science and Technology of China Hefei, Hefei 230026, ChinaDepartment of Automation, University of Science and Technology of China Hefei, Hefei 230026, ChinaDepartment of Automation, University of Science and Technology of China Hefei, Hefei 230026, ChinaDepartment of Automation, University of Science and Technology of China Hefei, Hefei 230026, ChinaThe location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf.https://www.mdpi.com/2072-4292/13/8/1600air-quality inferencespatiotemporal interactiongraph convolutional networksstation location recommendationincremental learning |
| spellingShingle | Yu Kang Jie Chen Yang Cao Zhenyi Xu A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station Remote Sensing air-quality inference spatiotemporal interaction graph convolutional networks station location recommendation incremental learning |
| title | A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station |
| title_full | A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station |
| title_fullStr | A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station |
| title_full_unstemmed | A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station |
| title_short | A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station |
| title_sort | higher order graph convolutional network for location recommendation of an air quality monitoring station |
| topic | air-quality inference spatiotemporal interaction graph convolutional networks station location recommendation incremental learning |
| url | https://www.mdpi.com/2072-4292/13/8/1600 |
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